Kits and methods to distinguish false labor and true labor

ABSTRACT

Kits and methods to distinguish between false and true labor are provided. The kits and methods can utilize differences in abundance and/or differences in the rate of change in abundance of B7-H2, SORC2, TF, C1-Esterase Inhibitor, Ran, IMDH1 and/or PGAM1, as markers of true labor.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a divisional of co-pending U.S. patent applicationSer. No. 15/773,980, filed May 4, 2018; which is the U.S. National PhaseApplication of International Patent Application No. PCT/US2016/060819,which was filed on Nov. 7, 2016; which claims priority to and thebenefit of the earlier filing date of U.S. Provisional PatentApplication No. 62/251,517 filed on Nov. 5, 2015. Each of these earlierfiled applications is incorporated herein by reference in its entiretyas if fully set forth herein.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH

This invention was made with government support under HHSN275201300006Cawarded by the National Institutes of Health. The government has certainrights in the invention.

FIELD OF THE DISCLOSURE

Kits and methods to distinguish between false and true labor areprovided. The kits and methods can utilize differences in the rate ofchange in abundance of B7-H2, SORC2, TF, and C1-Esterase Inhibitoracross at least two measures during pregnancy, as markers of true labor.Additionally, the kits and methods can utilize changes in abundance ofTF, Ran, IMDH1, and PGAM1, as markers of true labor.

BACKGROUND OF THE DISCLOSURE

The diagnosis of labor is one of the great challenges in obstetricalcare and is always retrospective. Despite the progress of modernmedicine, clinical symptoms (i.e. pain, leakage of amniotic fluid orexpulsion of the cervical mucus plug) and cervical examination (i.e.Bishop Score) are still the primary criteria used to diagnose truelabor. Yet, the Bishop score which is predominantly influenced by thedegree of cervical dilatation and effacement is a poor predictor of truelabor (Rozeboom et al., The Journal of reproductive medicine 1989;34(4):285-8; Lim et al., Journal of clinical ultrasound: JCU 1992;20(9):599-603; and Berghella et al., American journal of obstetrics andgynecology 1997; 177(4):723-30).

Overdiagnosis of labor occurs at full-term (during or after the 37^(th)week of gestation) as well as preterm (before the 37^(th) week ofgestation). The imprecise diagnosis of labor at full-term or preterm canlead to an increase in hospitalization, psychological stress, cost andunnecessary medical interventions such as analgesia and labor inductionor augmentation. The increased risk of labor induction and augmentationcaused by misdiagnosis of labor can be particularly harmful if themisdiagnosis occurs during preterm, when the fetus is not yet fullydeveloped. Yet, overdiagnosis of labor occurs in up to 40% of womenpresenting with preterm labor symptoms. Currently, there are nodiagnostic tests to determine if a woman is having true versus falselabor.

SUMMARY OF THE DISCLOSURE

The present disclosure describes kits and methods to distinguish truefrom false labor, with specificity as high as 94%. The high specificityof these labor tests means that in the presence of a negative testresult, patients are highly unlikely to deliver. Therefore, unnecessaryhospital admissions, medical interventions, and costs can be avoided.

The current disclosure provides that seven biomarkers that can be foundin maternal blood plasma predict the onset of true labor (also referredto as active labor) or show that true labor is in progress. Thesemarkers include:

Name UniProt ID ICOS Ligand (B7-H2) O75144 VPS10 Domain ContainingReceptor SorCS2 (SORC2) Q96PQ0 Tissue Factor (TF) P13726 Plasma ProteaseC1 Inhibitor (C1-Esterase Inhibitor) P05155 Ras-related Nuclear protein(Ran) P62826 Inosine-5′-monophosphate dehydrogenase (IMDH1) P20839Phosphoglycerate mutase 1 (PGAM1) P18669

More particularly, two models for the prediction of true labor have beendeveloped. In the first model, differences in the rate of change ofprotein abundance of four markers (B7-H2, SORC2 TF, and C1-EsteraseInhibitor) across at least two measures are indicative of true labor, ora lack thereof. In the second model, changes in the abundance of fourmarkers (TF, Ran, IMDH1, and PGAM1) at patient admission are indicativeof true labor, or a lack thereof. The two models can be usedindividually or in combination.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1. Exemplary sequence of ICOS Ligand (B7-H2; SEQ ID NO: 1).

FIG. 2. Exemplary sequence of VPS10 Domain Containing Receptor SorCS2(SORC2: SEQ ID NO: 2).

FIG. 3. Exemplary sequence of Tissue Factor (TF; SEQ ID NO: 3).

FIG. 4. Exemplary sequence of Plasma Protease C1 Inhibitor (C1-Esterase;Inhibitor; SEQ ID NO: 4).

FIG. 5. Exemplary sequence of Ras-related Nuclear protein (Ran) (SEQ IDNO: 5).

FIG. 6. Exemplary sequence of Inosine-5′-monophosphate dehydrogenase(IMDH1; SEQ ID NO: 6).

FIG. 7. Exemplary sequence of Phosphoglycerate mutase 1 (PGAM1; SEQ IDNO: 7).

FIG. 8. Clinical characteristics of the study population.

FIGS. 9A-9B. Longitudinal protein abundance profiles for TF (FIG. 9A)and B7-H2 (FIG. 9B) in 20 patients who delivered with spontaneous laborat term (TIL, gray) and 20 patients who delivered without spontaneouslabor at term (TNL, light gray). Protein abundance is expressed inrelative fluorescence units.

FIGS. 10A-10E. Longitudinal protein abundance profiles for SORC2 (FIG.10A), C1-Esterase Inhibitor (FIG. 10B), Ran (FIG. 10C), PGAM1 (FIG.10D), and IMDH1 (FIG. 10E) in 20 patients who delivered with spontaneouslabor at term (TIL, dark gray) and 20 patients who delivered withoutspontaneous labor at term (TNL, light gray). Protein abundance isexpressed in relative fluorescence units.

FIG. 11. Receiver Operating Characteristic curves of a four-proteinclassifier (black) and for B7-H2 alone (gray) obtained when predictingspontaneous labor using the rate of change in protein abundancedetermined from two serial measurements taken between 33 and 40 weeks ofgestation. Area under the ROC curve was 0.94 for the four proteins whileB7-H2 alone achieved 0.88.

FIG. 12. B7-H2 protein abundance and slopes calculation. Left and rightpanels show B7-H2 protein abundance (log 2 of) in relative fluorescenceunits, as a function of gestational age at the last two samples in 20TIL and 20 TNL patients. The middle panel shows a boxplot representationof the slopes defined as (log 2 abundance at last sample−log 2 abundanceat second to last sample)/(GA at last sample−GA at second to lastsample). The boxes represent the interquartile range, the thick linebeing the median value. Whiskers extend up to the extreme values if notmore than 1.5 time the IQR. TIL=term in labor, TNL=term not in labor,GA=gestational age.

FIG. 13. Decision zones based on the four-protein slope model. Theslopes of the proteins in the gestational age interval 33 to 40 weeksare first calculated as described and illustrated in FIG. 12. A riskscore is computed from the slopes of the four proteins as:Score=4.422*S(B7−H2)−5.981*S(SORC2)−0.538*S(TF)−0.033*S(C1-EsteraseInhibitor)+1.314, where S stands for the slope of protein abundanceestimated in the 33 to 40 weeks of gestation interval. Zone A:Score≤−1.14; Zone B: −1.14<Score≤0; Zone C: Score>0.

REFERENCE TO SEQUENCE LISTING

The amino acid sequences described herein are shown using standardletter abbreviations, as defined in 37 C.F.R. § 1.822. A computerreadable text file, entitled “2L73589.txt (Sequence Listing.txt)”created on or about Nov. 10, 2021, with a file size of 28 KB, containsthe Sequence Listing for this application and is hereby incorporated byreference in its entirety.

DESCRIPTION

What is Labor: During pregnancy the uterus undergoes two majorprocesses, the first is a substantial growth that is characterized byhypertrophy and hyperplasia of the myometrium and the second process isthe quiescence of the myometrium that is believed to be a balancebetween the hormonal effect of progesterone and the degree of stretchingof the myometrial cells what is known as phase zero of parturition (orchildbirth). The transition from quiescent to labor activates the commonpathway of parturition, and includes myometrium contractions, cervicalripening (effacement and dilatation) and activation of the decidua andchorioamniotic membranes often presented as rupture of the membranes.However, these changes may sometimes be transient, for example, 40-60%of patients presenting with premature contractions will deliver at term.Even at term, a large fraction of patients may present with contractionsand/or certain degree of cervical effacement and dilatation, but wouldnot progress into active labor. The current diagnostic modalities forthe identification of the labor process include physical examination ofthe uterus and the pelvis and tocometry. These modalities are notsensitive enough and often result in a false definition of labor,leading to unnecessary hospitalization and medical interventions thatput the patient at increased risk for cesarean section (MacDorman etal., Clinics in perinatology 2008; 35(2):293-307; Gregory et al.,American journal of perinatology 2012; 29(1):7-18). Recently, the use oftransvaginal sonography for cervical length measurement with a cutoff of1.5 cm has been proposed to be used for the differentiation between trueand false labor in term patients. However this method had only 84%specificity and 67% sensitivity, a positive likelihood ratio of 4.2 anda negative likelihood ratio of 0.39 for the identification of true laborin patients at term (Kunzier et al., American journal of obstetrics andgynecology 2016(3):372.e1-5). Thus, a robust and novel diagnostic toolto assist in the identification of patients who are truly going intolabor is needed.

Biomarkers for Labor: Labor is a complex process characterized bysystemic and local changes in inflammatory, hematologic, and hormonalprocesses. In recent years, high-dimensional biology was employed toidentify the metabolome, transcriptome, and proteome of all thecomponents of the common pathway of parturition.

In one metabolomic study of parturition, eighteen urine metabolitesdetermined by gas chromatograph/mass spectrometry (GC/MS) and nuclearmagnetic resonance spectroscopy (NMR) were found to be able todifferentiate between women during labor and those before labor (Caboniet al., The journal of maternal-fetal & neonatal medicine 2014; 27 Suppl2:4-12). In addition, the metabolomics analysis showed that thesyntheses as well as degradation of ketone bodies were the most relevantbiochemical pathways in urine samples from patients with labor.

Several transcriptome reports showed that spontaneous labor has a uniquegene expression profile of the myometrium (e.g. Chan et al., The Journalof clinical endocrinology and metabolism 2002; 87(6):2435-41), cervix(e.g. Hassan et al., American journal of obstetrics and gynecology 2006;195(3):778-86), and chorioamniotic membranes (e.g. Haddad et al.,American journal of obstetrics and gynecology 2006; 195(2):394 e1-24)that differ from that of patients without labor.

Many studies have used proteomic methods to attempt to identifybiomarkers for preterm labor, and other obstetrical complications.Proteomics is the large-scale analysis of proteins including theirexpression profiles, structures and functions. It is a technique that isfrequently used for the development of biomarkers in clinical medicine.This is because it studies the final gene product (a specific protein)which is more complex and closer to biological function than the geneitself.

One proteomics study of parturition demonstrated a significant change inthe proteomic profile of the cervical vaginal fluid in women prior tolabor at term (Di Quinzio et al., Journal of proteome research 2008;7(5):1916-21; Heng et al., Frontiers in physiology. 2015; 6:151).However, few studies have reported on different methods to distinguishtrue from false labor at term. Furthermore, the maternal plasma proteomeprior to labor at term has not been reported thus far.

The present disclosure describes kits and methods to distinguish truefrom false labor, with specificity as high as 94%. The high specificityof these labor tests means that in the presence of a negative testresult, patients are highly unlikely to deliver. Therefore, unnecessaryhospital admissions, medical interventions, and costs can be avoided.

The current disclosure provides that measures of seven markers that canbe found in maternal blood plasma can demonstrate that true labor is inprocess. These markers include:

Name UniProt ID ICOS Ligand (B7-H2) O75144 VPS10 Domain ContainingReceptor SorCS2 (SORC2) Q96PQ0 Tissue Factor (TF) P13726 Plasma ProteaseC1 Inhibitor (C1-Esterase Inhibitor) P05155 Ras-related Nuclear protein(Ran) P62826 Inosine-5′-monophosphate 1 dehydrogenase (IMDH1) P20839Phosphoglycerate mutase 1 (PGAM1) P18669

More particularly, two models for the prediction of true versus falselabor have been developed. In the first model, differences in the rateof change of protein abundance of four markers (B7-H2, SORC2 TF, andC1-Esterase Inhibitor) across at least two measures are indicative oftrue labor, or a lack thereof. In the second model, changes in theabundance of four markers (TF, Ran, IMDH1, and PGAM1) are indicative oftrue labor, or a lack thereof.

The tests can be performed based on biological samples taken when womenare admitted to the hospital due to an episode of suspected labor.Levels of the markers can be compared to a measure previously taken fromthe same woman, and changes in abundance (e.g., slope) can becalculated. In particular embodiments, a biological sample is plasma orserum. In particular embodiments, a biological sample may be obtained atan earlier timepoint in a pregnancy (e.g., the beginning of the thirdtrimester) and when true labor is suspected, but not confirmed. Inparticular embodiments, a biological sample may be obtained at anearlier timepoint in a pregnancy (e.g., the beginning of the thirdtrimester) and when false labor is suspected, but not confirmed.

In particular embodiments, the kits and methods are used to identifytrue labor when a pregnant female is experiencing symptoms of labor.Symptoms of labor can include cramps and back pain, diarrhea, lethargy,dilation of the cervix, and contractions. Contractions can be defined asthe periodic tightening and relaxing of the uterine muscle. Contractionscan be a sign of true labor, or can be associated with false labor.

In particular embodiments, the kits and methods to identify true laborinclude measuring B7-H2. B7 is a family of membrane proteins that areexpressed by antigen presenting cells. B7 binds to proteins expressed onthe surface of T cells and can either enhance or dampen activation of Tcells upon engagement of a T cell receptor with its cognate antigen andmajor histocompatibility complex (MHC or human leukocyte antigencomplex, HLA). There are several members of the B7 protein familyincluding: B7-1, B7-2, B7-DC, and B7-H1 through H7. B7-H2 (also known asICOSLG, B7RP1 and CD275) interacts with ICOS and/or CD28 expressed by Tcells to provide a costimulatory signal to T cells, which can contributeto T cell activation upon T cell receptor engagement with an antigen/MHCcomplex. An exemplary sequence of human B7-H2 (UniProt ID 075144) isGenBank Accession number AF289028 (see FIG. 1, SEQ ID NO: 1).

In particular embodiments, the kits and methods to identify true laborinclude measuring SORC2. SORC2 (also known as SORCS2) is a member of thevacuolar protein sorting 10 (VPS10) domain-containing receptor proteins.In mammals, VPS10 family proteins such as SORC2 are predominantlyexpressed in the central nervous system. An exemplary sequence of humanSORC2 (UniProt ID Q96PQ0) is GenBank Accession number NM_020777.2 (seeFIG. 2, SEQ ID NO: 2).

In particular embodiments, the kits and methods to identify true laborinclude measuring TF. TF (also known as platelet tissue factor, factorIII, thromboplastin and CD142) is a membrane protein that initiatesblood coagulation, which can be caused by tissue damage or inflammation.TF is a member of the cytokine receptor protein family, which includesmembrane proteins that have an extracellular domain that can bind toextracellular factors to initiate signaling, a transmembrane domain, anda cytosolic domain that can transmit a signal induced by anextracellular interaction. The extracellular domain of TF binds tofactor Vila to initiate a coagulation signaling cascade. Exemplarysequences of human TF (UniProt ID P13726) include GenBank Accessionnumbers J02931.1 and M16553 (see FIG. 3, SEQ ID NO: 3).

In particular embodiments, the kits and methods to identify true laborinclude measuring C1-Esterase Inhibitor. C1-Esterase Inhibitor (alsoknown as C1-Inhibitor and Plasma Protease C1 Inhibitor) is a regulatorof the complement pathway and can prevent spontaneous activation ofcomplement. The complement pathway is part of the innate immune systemand can cause destruction of cellular membranes leading to death ofcells such as virus-infected cells, bacteria, and fungi. C1-EsteraseInhibitor is a member of the serpin protein family. Serpin proteins canirreversibly inhibit proteases by binding to and inducing aconformational change in the protease active site. C1-Esterase Inhibitorbinds and irreversibly blocks the activity of the complement pathwayproteases C1r and C1s. An exemplary sequence of human C1-EsteraseInhibitor (UniProt ID P05155) is GenBank Accession number M13690 (seeFIG. 4, SEQ ID NO: 4).

In particular embodiments, the kits and methods to identify true laborinclude measuring Ran (also known as ARA24). Ran is a small GTPase thattranslocates RNA and proteins through the nuclear pore complex.Regulation of Ran-mediated transport through the nuclear pore complex iscontrolled by whether Ran is bound to GTP or GDP. An exemplary sequenceof human Ran (UniProt ID P62826) is GenBank Accession number AF052578(see FIG. 5, SEQ ID NO: 5).

In particular embodiments, the kits and methods to identify true laborinclude measuring IMDH1. IMDH1 is an enzyme that convertsinosine-5′-monophosphate to xanthosine-5′-monophosphate, which is a ratelimiting step in the production of guanine nucleotides, such as GMP,GDP, and GTP. An exemplary sequence of human IMDH1 (UniProt ID P20839)is GenBank Accession number J05272 (see FIG. 6, SEQ ID NO: 6).

In particular embodiments, the kits and methods to identify true laborinclude measuring PGAM1 (also known as PGAM-B). PGAM1 is an enzyme thatcatalyzes a step in glycolysis, specifically, the conversion of3-phosphoglycerate (3PG) to 2-phosphoglycerate (2PG). An exemplarysequence of human PGAM1 (UniProt ID P18669) is GenBank Accession numberJ04173 (see FIG. 7, SEQ ID NO: 7).

B7-H2, SORC2, TF, C1-Esterase Inhibitor, Ran, IMDH1, and PGAM1 are“biomarkers” or “markers” in the context of the present disclosure.Biomarkers include the protein forms of the markers as well asassociated nucleic acids, oligonucleotides, and metabolites, togetherwith their related metabolites, mutations, isoforms, variants,polymorphisms, modifications, fragments, subunits, degradation products,elements, and other analytes or sample-derived measures. Biomarkers canalso include mutated proteins, mutated nucleic acids, variations in copynumbers, and/or transcript variants. Biomarkers also encompasscombinations of any one or more of the foregoing measurements, includingtemporal trends and differences. Particular embodiments of biomarkersinclude B7-H2 (SEQ ID NO: 1); SORC2 (SEQ ID NO: 2); TF (SEQ ID NO: 3);C1-Esterase Inhibitor (SEQ ID NO: 4); Ran (SEQ ID NO: 5); IMDH1 (SEQ IDNO: 6); and/or PGAM1 (SEQ ID NO: 7).

Protein expression patterns can be evaluated using any method thatprovides a quantitative measure and is suitable for evaluation ofmultiple markers extracted from samples. Exemplary methods include:ELISA sandwich assays, mass spectrometric detection, calorimetricassays, binding to a protein array (e.g., antibody array), orfluorescent activated cell sorting (FACS). Approaches can use labeledaffinity reagents (e.g., antibodies, small molecules, etc.) thatrecognize epitopes of one or more protein products in an ELISA, antibodyarray, or FACS screen.

In particular embodiments, the true labor markers can be measured usingimmunoassay techniques. Immunoassays are laboratory procedures thatutilize antibodies and/or antigens to detect a molecule. In particularembodiments, the true labor markers can be detected using antibody-basedtechniques. In particular embodiments, B7-H2 can be detected using ananti-B7-H2 antibody. An example of a commercially available anti-B7-H2antibody is mouse-anti-B7-H22D3, available from BioLegend. In particularembodiments, SORC2 can be detected using an anti-SORC2 antibody. Anexample of a commercially available anti-SORC2 antibody is mouseanti-SORCS2 ab88330, available from Abcam. In particular embodiments, TFcan be detected using an anti-TF antibody. An example of a commerciallyavailable anti-TF antibody is mouse anti-Tissue Factor ab48647,available from Abcam. In particular embodiments, C1-Esterase Inhibitorcan be detected using an anti-C1-Esterase Inhibitor antibody. An exampleof a commercially available anti-C1 Esterase Inhibitor antibody is mouseanti-C1 Inactivator EPR8015, available from Abcam. In particularembodiments, Ran can be detected using an anti-Ran antibody. An exampleof a commercially available anti-Ran antibody is mouse anti-Ran ab11693,available from Abcam. In particular embodiments, IMDH1 can be detectedusing an anti-IMDH1 antibody. An example of a commercially availableanti-IMDH1 antibody is rabbit anti-IMDPH1, available from ThermoFisherScientific. PGAM1 can be detected using an anti-PGAM1 antibody. Anexample of a commercially available anti-PGAM1 Inhibitor antibody israbbit polyclonal anti-PGAM1 ab96622, available from Abcam.

Protein detection can include detection of full-length proteins, proteinfragments, mature proteins, pre-proteins, polypeptides, isoforms,mutations, variants, post-translationally modified proteins, andvariants thereof, and can be detected in any suitable manner. Levels ofbiomarkers can be determined at the protein level, e.g., by measuringthe serum levels of proteins. Such methods are well-known in the art andinclude, e.g., immunoassays based on antibodies to proteins encoded bythe genes, aptamers, or molecular imprints. Any biological material canbe used for the detection/quantification of the protein or its activity.Alternatively, a suitable method can be selected to determine theactivity of proteins. Such assays include protease assays, kinaseassays, phosphatase assays, and reductase assays, among many others.

Variants of the sequences disclosed and referenced herein are alsoincluded. Variants of peptides can include those having one or moreconservative amino acid substitutions. As used herein, a “conservativesubstitution” involves a substitution found in one of the followingconservative substitutions groups: Group 1: Alanine (Ala), Glycine(Gly), Serine (Ser), Threonine (Thr); Group 2: Aspartic acid (Asp),Glutamic acid (Glu); Group 3: Asparagine (Asn), Glutamine (Gln); Group4: Arginine (Arg), Lysine (Lys), Histidine (His); Group 5: Isoleucine(Ile), Leucine (Leu), Methionine (Met), Valine (Val); and Group 6:Phenylalanine (Phe), Tyrosine (Tyr), Tryptophan (Trp).

Additionally, amino acids can be grouped into conservative substitutiongroups by similar function or chemical structure or composition (e.g.,acidic, basic, aliphatic, aromatic, sulfur-containing). For example, analiphatic grouping may include, for purposes of substitution, Gly, Ala,Val, Leu, and Ile. Other groups containing amino acids that areconsidered conservative substitutions for one another include:sulfur-containing: Met and Cysteine (Cys); acidic: Asp, Glu, Asn, andGln; small aliphatic, nonpolar or slightly polar residues: Ala, Ser,Thr, Pro, and Gly; polar, negatively charged residues and their amides:Asp, Asn, Glu, and Gln; polar, positively charged residues: His, Arg,and Lys; large aliphatic, nonpolar residues: Met, Leu, Ile, Val, andCys; and large aromatic residues: Phe, Tyr, and Trp. Additionalinformation is found in Creighton (1984) Proteins, W.H. Freeman andCompany.

Variants of the protein and nucleic acid sequences disclosed orreferenced herein also include sequences with at least 70% sequenceidentity, 80% sequence identity, 85% sequence, 90% sequence identity,95% sequence identity, 96% sequence identity, 97% sequence identity, 98%sequence identity, or 99% sequence identity to the protein and nucleicacid sequences disclosed or referenced herein and particularly includingSEQ ID NOs:1-7.

“% sequence identity” or “% identity” refers to a relationship betweentwo or more sequences, as determined by comparing the sequences. In theart, “identity” also means the degree of sequence relatedness betweenproteins or nucleic acid sequences as determined by the match betweenstrings of such sequences. “Identity” (often referred to as“similarity”) can be readily calculated by known methods, includingthose described in: Computational Molecular Biology (Lesk, A. M., ed.)Oxford University Press, NY (1988); Biocomputing: Informatics and GenomeProjects (Smith, D. W., ed.) Academic Press, NY (1994); ComputerAnalysis of Sequence Data, Part I (Griffin, A. M., and Griffin, H. G.,eds.) Humana Press, N J (1994); Sequence Analysis in Molecular Biology(Von Heijne, G., ed.) Academic Press (1987); and Sequence AnalysisPrimer (Gribskov, M. and Devereux, J., eds.) Oxford University Press, NY(1992). Preferred methods to determine identity are designed to give thebest match between the sequences tested. Methods to determine identityand similarity are codified in publicly available computer programs.Sequence alignments and percent identity calculations may be performedusing the Megalign program of the LASERGENE bioinformatics computingsuite (DNASTAR, Inc., Madison, Wis.). Multiple alignment of thesequences can also be performed using the Clustal method of alignment(Higgins and Sharp CABIOS, 5, 151-153 (1989)) with default parameters(GAP PENALTY=10, GAP LENGTH PENALTY=10). Relevant programs also includethe GCG suite of programs (Wisconsin Package Version 9.0, GeneticsComputer Group (GCG), Madison, Wis.); BLASTP, BLASTN, BLASTX (Altschul,et al., J. Mol. Biol. 215:403-410 (1990)); DNASTAR (DNASTAR, Inc.,Madison, Wis.); and the FASTA program incorporating the Smith-Watermanalgorithm (Pearson, Comput. Methods Genome Res., [Proc. Int. Symp.](1994), Meeting Date 1992, 111-20. Editor(s): Suhai, Sandor. Publisher:Plenum, New York, N.Y.). Within the context of this disclosure it willbe understood that where sequence analysis software is used foranalysis, the results of the analysis are based on the “default values”of the program referenced. As used herein “default values” will mean anyset of values or parameters which originally load with the software whenfirst initialized.

Using sequence information provided by public database entries for thebiomarkers described herein, expression of the biomarker can be detectedand measured using techniques well-known to those of skill in the art.For example, nucleic acid sequences in the sequence databases thatcorrespond to nucleic acids of biomarkers can be used to constructprimers and probes for detecting and/or measuring biomarker nucleicacids. These probes can be used in, e.g., Northern or Southern blothybridization analyses, ribonuclease protection assays, and/or methodsthat quantitatively amplify specific nucleic acid sequences. As anotherexample, sequences from sequence databases can be used to constructprimers for specifically amplifying biomarker sequences in, e.g.,amplification-based detection and quantitation methods such asreverse-transcription based polymerase chain reaction (RT-PCR) and PCR.When alterations in gene expression are associated with geneamplification, nucleotide deletions, polymorphisms, post-translationalmodifications and/or mutations, sequence comparisons in test andreference populations can be made by comparing relative amounts of theexamined DNA sequences in the test and reference populations.

A number of methods for obtaining expression data can be used singly orin combination for determining expression patterns and profiles in thecontext of the present disclosure. For example, DNA and RNA expressionpatterns can be evaluated by northern analysis, PCR, RT-PCR,quantitative real-time RT-PCR analysis with TaqMan assays, FRETdetection, monitoring one or more molecular beacon, hybridization to anoligonucleotide array, hybridization to a cDNA array, hybridization to apolynucleotide array, hybridization to a liquid microarray,hybridization to a microelectric array, molecular beacons, cDNAsequencing, clone hybridization, cDNA fragment fingerprinting, serialanalysis of gene expression (SAGE), subtractive hybridization,differential display and/or differential screening.

Gene expression changes can be related to epigenetic variations (e.g.DNA methylation). Epigenetic regulation mechanisms do not involve achange to the DNA sequence. Instead, epigenetic variations includecovalent modification of DNA, RNA, and the proteins associated with DNA.These in turn can result in changes to the conformation of DNA andaccessibility of regulators to the DNA. Such changes cannot beidentified simply by gene sequencing. Janssen, et al., Particle andFibre Toxicology, 10:22 (2013) studied methylation in placental tissueusing methods published by Tabish, et al., PLoS ONE 2012, 7:e34674 andby Godderis, et al., Epigenomics 4:269-277 (2012). MS-MLPA(Methylation-specific Multiplex ligation-dependent probe amplification)can be used to study methylation status of specific genes, for examplein Proctor, et al., Clin. Chem. 52:1276-1283 (2006). Materials andmethods for MS-MLPA as used in published studies can be obtained fromMRC-Holland, Amsterdam, The Netherlands. Additional methods are reviewedand compared in Shen, et al., Curr. Opin. Clin. Nutr. Metab. Care.10:576-81 (2007); Gu et al., Nature Methods 7:133-138 (2010); Bock etal., Nature Biotech. 28:1106-1114 (2010); and Harris et al., NatureBiotech. 28:1097-1105 (2010).

In particular embodiments, the kits and methods to distinguish betweenfalse and true labor include use of an array to measure markers. Avariety of solid phase arrays can also be employed to determineexpression patterns. Exemplary formats include membrane or filter arrays(e.g., nitrocellulose, nylon), pin arrays, and bead arrays (e.g., in aliquid “slurry”). Essentially any solid support capable of withstandingthe reagents and conditions necessary for performing the particularexpression assay can be utilized. For example, functionalized glass,silicon, silicon dioxide, modified silicon, any of a variety ofpolymers, such as (poly)tetrafluoroethylene, (poly)vinylidenedifluoride,polystyrene, polycarbonate, or combinations thereof can all serve as thesubstrate for a solid phase array.

In particular embodiments, arrays can include “chips” composed, e.g., ofone of the above-specified materials. Polynucleotide probes, e.g., RNAor DNA, such as cDNA, synthetic oligonucleotides, and the like, orbinding proteins such as antibodies or antigen-binding fragments orderivatives thereof, that specifically interact with expression productsof individual components of the candidate library are affixed to thechip in a logically ordered manner, i.e., in an array. In addition, anymolecule with a specific affinity for either the sense or anti-sensesequence of the marker nucleotide sequence (depending on the design ofthe sample labeling), can be fixed to the array surface without loss ofspecific affinity for the marker and can be obtained and produced forarray production, for example, proteins that specifically recognize thespecific nucleic acid sequence of the marker, ribozymes, peptide nucleicacids (PNA), or other chemicals or molecules with specific affinity.

Detailed discussion of methods for linking nucleic acids and proteins toa chip substrate, are found in, e.g., U.S. Pat. Nos. 5,143,854;6,087,112; 5,215,882; 5,707,807; 5,807,522; 5,958,342; 5,994,076;6,004,755; 6,048,695; 6,060,240; 6,090,556; and 6,040,138.

Microarray expression may be detected by scanning the microarray with avariety of laser or CCD-based scanners, and extracting features withsoftware packages, for example, Imagene (Biodiscovery, Hawthorne,Calif.), Feature Extraction Software (Agilent), Scanalyze (Eisen, M.1999. SCANALYZE User Manual; Stanford Univ., Stanford, Calif. Ver2.32.), or GenePix (Axon Instruments).

“Measuring” includes determining, assessing, calculating, and/oranalyzing a value or set of values associated with a sample bymeasurement of marker (i.e., analyte) levels in the sample.“Determining” may further include comparing levels against constituentlevels in a sample or set of samples from the same subject or othersubject(s). The B7-H2, SORC2, TF, C1-Esterase Inhibitor, Ran, IMDH1,PGAM1 and/or other biomarkers of the present disclosure can bedetermined by any of various conventional methods known in the art.

In particular embodiments, quantitative data obtained for the markers ofinterest and other dataset components can be subjected to an analyticprocess with chosen parameters. The parameters of the analytic processmay be those disclosed herein or those derived using the guidelinesdescribed herein. The analytic process used to generate a result may beany type of process capable of providing a result useful for classifyinga sample, for example, comparison of the obtained dataset with areference dataset, a linear algorithm, a quadratic algorithm, a decisiontree algorithm, or a voting algorithm. The analytic process may set athreshold for determining the probability that a sample belongs to agiven class (false labor or true labor). The probability preferably isat least 50%, or at least 60%, or at least 70%, or at least 80%, or atleast 90% or higher. For example, particular embodiments utilize theformula and classification scale described in FIG. 13.

A “dataset” as used herein is a set of numerical values resulting fromevaluation of a sample (or population of samples) under a desiredcondition. The values of the dataset can be obtained, for example, byexperimentally obtaining measures from a sample and constructing adataset from these measurements; or alternatively, by obtaining adataset from a service provider such as a laboratory, or from a databaseor a server on which the dataset has been stored.

In particular embodiments of the present disclosure, a dataset of valuesis determined by measuring biomarkers from a non-pregnant subject or apregnant subject who is not in labor. Datasets can be used by aninterpretation function to derive a true labor score, which can providea quantitative measure of likelihood that a subject is in true labor.

The sensitivity of a diagnostic measure is also referred to as the truepositive rate, or the recall in some fields. It denotes the proportionof positive results (true labor occurring) that are correctly identifiedas such. The specificity of a diagnostic measure is also referred to asthe true negative rate. It denotes the proportion of negatives that arecorrectly identified as such (false labor occurring).

In particular embodiments, the kits and methods disclosed herein have atleast a 59% sensitivity and a 100% specificity. In particularembodiments, the kits and methods disclosed herein have at least a 75%sensitivity and a 100% specificity. In particular embodiments, the kitsand methods disclosed herein true labor is identified with a specificityof 80% or greater, 81% or greater, 82% or greater, 83% or greater, 84%or greater, 85% or greater, 86% or greater, 87% or greater, 88% orgreater, 89% or greater, 90% or greater, 91% or greater, 92% or greater,93% or greater, 94% or greater, 95% or greater, 96% or greater, 97% orgreater, 97% or greater, 98% or greater or 99% or greater.

A used herein, the term “change of abundance” with regard to changes inthe marker levels can refer to an increase of more than 20%, more than30%, more than 40%, more than 50%, more than 60%, more than 70%, morethan 80%, more than 90%, more than 100%, more than 150%, or more than200% compared to a reference level. In particular embodiments, “changeof abundance” measures can be evaluated independently against areference level without consideration of earlier comparisons in the samesubject. In particular embodiments, “change of abundance” can refer toan increase of at least 1.5 times over a previous measure or as comparedto a particular population reference level.

As used herein, “unchanged” measures are evaluated in relation to aprevious comparison in the same subject and denote a failure to achievea statistically significant change in a score towards or away from areference level in the particular subject.

In particular embodiments, the amount of the biomarker(s) can bemeasured in a sample and used to derive a true labor score, which truelabor score is then compared to a “reference level”. Reference levelscan include “normal”, “control”, “non-labor” or “false labor” levels orvalues, defined according to, e.g., discrimination limits or riskdefining thresholds, in order to define cut-off points and/or abnormalvalues for true labor. The reference level then is the level of one ormore biomarkers or combined biomarker indices typically found in asubject who is not in true labor. Other terms for “reference levels”include “index,” “baseline,” “standard,” “pre-labor”, etc. Such normallevels can vary, based on whether a biomarker is used alone or in aformula combined with other biomarkers to output a score. Alternatively,the reference level can be a database of biomarker patterns frompreviously tested subjects who did not go into labor over a clinicallyrelevant time period. Reference levels can also be derived from, e.g., acontrol subject or population whose time of onset of labor is known. Inparticular embodiments, the reference value can be derived from one ormore subjects who did not experience an onset of spontaneous labor, sucha patient who underwent elective cesarean delivery. In particularembodiments, reference levels can be derived from the patient undergoinga true labor score analysis, wherein the reference level samples areobtained from said patient at a previous time-point.

In particular embodiments, “reference level” can refer to a standardizedvalue for the markers which represents a level not associated with onsetof labor. The reference level can be a universal reference level whichis useful across a variety of testing locations or can be a referencelevel specific for the testing location and specific immunoassay used tomeasure the true labor markers. In particular embodiments, the referencelevels of the true labor markers and/or reference weighted score isderived from (i) an individual; (ii) a group of individuals; (iii) asubject before pregnancy; or (iv) a pregnant subject not yet in labor;wherein the samples are obtained at time-points when each individual wasnot in true labor. In particular embodiments, the subject whose samplesare used to obtain a reference level can be different from the subjectwho is being tested for true labor. In particular embodiments, thesubject whose samples are used to obtain a reference level can be thesame subject who is being tested for true labor. When the referencelevel is based on samples collected from the same subject, referencelevel samples can be collected at earlier time-points, either beforepregnancy or at earlier time-points during pregnancy.

In particular embodiments, the term “rate of change of abundance” canrefer to a rate of change over time, or a slope. In particularembodiments, “rate of change of abundance” can refer to a rate of changein abundance of a true labor marker over time, such as during the courseof pregnancy. In particular embodiments, when a marker is measured at afirst time-point (T1) and a second time-point (T2), “rate of change ofabundance” can be defined as(Abundance_(T2)−Abundance_(T1))/(GA_(T2)−GA_(T1)), wherein GA refers togestational age and can be measured in weeks. In particular embodiments,the rate of change of abundance of a true labor marker is significantlychanged in an individual experiencing true labor, as compared to therate of change of abundance of said true labor marker in an individualwho is not experiencing true labor.

In particular embodiments, a “significantly changed slope” can refer toan increase or decrease of more than 20%, more than 30%, more than 40%,more than 50%, more than 60%, more than 70%, more than 80%, more than90%, more than 100%, more than 150%, or more than 200% compared to areference slope. In particular embodiments, “significantly changedslope” can be evaluated independently against a reference slope withoutconsideration of earlier comparisons in the same subject. In particularembodiments, “significantly changed slope” can be evaluated against areference slope that is calculated based on measured obtained earlierfrom the same subject.

In particular embodiments, a “reference slope” can refer to a slopecalculated from “normal”, “control”, “non-labor”, “pre-labor” or “falselabor” levels or values. In particular embodiments, a “reference slope”can be calculated using reference levels obtained from (i) anon-pregnant subject; or (ii) a pregnant subject not in labor. Inparticular embodiments, the subject whose samples are used to calculatea reference slope can be a distinct individual from the subject who isbeing tested for true labor. In particular embodiments, the subjectwhose samples are used to calculate the “reference slope” can be thesame subject who is being tested for true labor. When samples from thesame subject are used for the reference slope, the reference slopesamples can be collected at earlier time-points, either before pregnancyor at earlier time-points during pregnancy.

In particular embodiments, change of abundance or rate of change ofabundance can be used in a model to calculate a true labor score. Inparticular embodiments, the true labor score is calculated using alinear discriminant analysis (LDA) model. LDA is a method that can beused to classify data points into decision zones based on a linearcombination of features. In particular embodiments, the decision zonescan include true labor and false labor. In particular embodiments, LDAcan use a linear combination of the measurements of true labor markersto distinguish between decision zones, including true labor and falselabor. In particular embodiments, LDA can be used to calculate truelabor score (1), wherein rate of change of abundance (or slope) of truelabor markers can be used as input values in the model (see FIG. 13). Inparticular embodiments, LDA can be used to calculate true labor score(2), wherein change of abundance of true labor markers can be used asinput values in the model. In particular embodiments, a true labor scoreis an output value of the LDA. In particular embodiments, a true laborscore can be a numerical value, wherein a score above a particularthreshold value indicates true labor and a score below a particularthreshold value indicates false labor.

“Interpretation functions,” as used herein, means the transformation ofa set of observed data into a meaningful determination of particularinterest; e.g., an interpretation function may be a predictive modelthat is created by utilizing one or more statistical algorithms totransform a dataset of observed biomarker data into a meaningfuldetermination of likelihood of true labor in a subject.

Systems disclosed herein include kits to assay the biomarkers disclosedherein. Also disclosed herein are kits including one or more antibodies,binding proteins, primers and/or probes to assay the biomarkersdescribed herein. In particular embodiments, the kits may include one ormore containers containing one or more antibodies, binding proteins,primers and/or probes to be used to assay the biomarkers describedherein. Associated with such container(s) can be a safety notice. Inparticular embodiments, kits disclosed herein include antibodies,binding proteins, primers, probes, and amplification and detectionreagents, detectable labels or subsets thereof.

In particular embodiments, the kits may include instructions for usingthe kit in the methods disclosed herein. In particular embodiments, thekit may include instructions regarding preparation of the antibodies,binding proteins, primers and/or probes, use of the antibodies, bindingproteins, primers and/or probes, proper disposal of the related waste,interpretation of results, and the like. The instructions can be in theform of printed instructions provided inside a carton containing thekit. The instructions can also be printed on the carton and/or on otherportions of the kit. Instructions may be in the form of a sheet,pamphlet, brochure, CD-Rom, or computer-readable device, or can providedirections to instructions at a remote location, such as a website. Theinstructions may be in English and/or in any national or regionallanguage.

In particular embodiments, the kits described herein include some or allof the necessary supplies needed to use the kit, thereby eliminating theneed to locate and gather such supplies. The supplies can includepipettes, pipette tips, buffers, reagents, plates, films, tubes,thermocyclers, tube racks, gloves, sterilizing liquids, and the like.

The Example below is included to demonstrate particular embodiments ofthe disclosure. Those of ordinary skill in the art should recognize inlight of the present disclosure that many changes can be made to thespecific embodiments disclosed herein and still obtain a like or similarresult without departing from the spirit and scope of the disclosure.

Example 1. The rate of change of specific maternal plasma proteinabundance predicts spontaneous labor at term. The mechanism ofparturition is complex and still not completely understood. Labor per seis a retrospective diagnosis. The objective of this Example was todetermine whether changes in maternal plasma protein concentrations canpredict and/or define labor at term.

A longitudinal study was conducted including 2 separate datasets: 1)normal term pregnancy with (n=20) and without spontaneous labor (n=20);and 2) normal term pregnancy without spontaneous labor (n=50).Longitudinal maternal plasma samples were collected between 8-42 weeks.The gestational age at the last time point ranged between 37-40 weeks(median 39) while the second-to-last point ranged from 33-36 weeks(median: 35.5) for both groups (with and without spontaneous labor).Each dataset was obtained in a different experimental batch usingAptamer proteomics assays. The rate of change (slope) in proteinabundance between the last two samples available for each patient wascomputed. Linear discriminant analysis prediction models were builtusing up to four best predictors based either on the protein abundanceat the last time point or the slope across the last two time points. Onthe first dataset, a repeated (100 iterations) hold-out procedure wasused to build models on 75% of the subjects and test the models on tothe remaining 25% of the subjects to compute sensitivity, specificityand area under ROC curves.

The final models (one based on slopes and one on the abundance at lastpoint) were trained with the same methodology on 100% of the patientdata and then applied to the second dataset (women without spontaneouslabor) to obtain a second independent estimate of specificity of thesemodels.

When the slope of protein abundance between the last two time points ineach pregnancy was used, the cross-validated performance of fourproteins had a sensitivity of 60% and specificity of 94% (AUC=0.85). Theperformance of the final model had a sensitivity of 75% and specificityat 100% (AUC=0.94) as assessed by leave-one-out cross-validation basedon a fixed set of four proteins that included B7-H2, SORC2, TF, andC1-Esterase Inhibitor. B7-H2 contributed most to the predictive powerand had an AUC=0.88. When applied to a second dataset obtained in adifferent experimental batch, the model based on the slopes of fourproteins confirmed its high specificity (90%), unlike the model builtusing the protein abundance at the last time point only (specificity of78%).

This Example shows that determining the rate of change in abundance offour proteins after 33 weeks of gestation is predictive of the onset oflabor at term. The model has a high specificity which could be used todecrease the index of suspicion of labor. The top predictor among theseproteins was B7-H2 which is a ligand for the T-cell-specific cellsurface receptor ICOS.

Materials and Methods. Study design. A longitudinal study was conductedby searching the clinical database and Bank of Biologic samples of WayneState University, the Detroit Medical Center and the PerinatologyResearch Branch (NICHD/NIH). The inclusion criteria were: 1) singletongestation; 2) gestational age at delivery 37 weeks; and 3) the absenceof fetal malformations. Two separate datasets were included in thisstudy: 1) normal term pregnancy with (n=20) and without spontaneouslabor (n=20) (“data set 1”); and 2) normal term pregnancy withoutspontaneous labor (n=50) (“data set 2”). The majority of patients atterm without spontaneous labor had elective cesarean delivery (i.e. dueto prior cesarean delivery, malpresentation or non-reassuring fetalstatus).

Plasma samples were obtained at the time of each prenatal visit,scheduled at four-week intervals from the first or early secondtrimester until delivery. Each patient had at least three samplescollected at any of the following gestational age intervals (8-<16weeks, 16-<24 weeks, 24-<28 weeks, 28-<32 weeks, 32-<37 weeks and ≥37weeks) and a maximum of six samples were collected per patient.

All patients provided a written informed consent for their participationin the study. The use of biological specimens as well as clinical andultrasound data for research purposes were approved by the Wayne StateUniversity and Institutional Review Boards of NICHD.

Proteomics technique: The SOMAmer® (Slow Off-rate Modified Aptamers,SomaLogic Inc, Boulder, Colo.) binding reagents that allow themeasurement of over 1,125 proteins in maternal plasma samples were used(Gold et al., PloS one 2010; 5(12):e15004; Davies et al., PNAS 2012;109(49):19971-6; and SomaLogic.SOMAmer® Technical Notes. Proteomicsprofiling was performed by SomaLogic Inc who commercializes thetechnology and all needed reagents. The patient serum sample was dilutedand then incubated with the respective SOMAmer® mixes pre-immobilizedonto streptavidin (SA)-coated beads. The beads were washed to remove allnon-specifically associated proteins and other matrix constituents.Proteins that remained specifically bound to their cognate SOMAmer®reagents were tagged using an NHS-biotin reagent. After the labelingreaction, the beads were exposed to an anionic competitor solution thatprevents non-specific interactions from reforming after they aredisrupted. Essentially pure cognate-SOMAmer® complexes and unbound(free) SOMAmer® reagents are released from the SA beads usingultraviolet light that cleaves the photo-cleavable linker. Thephoto-cleavage eluate, which contains all SOMAmer® reagents (some boundto a biotin-labeled protein and some free), was separated from the beadsand then incubated with a second streptavidin-coated bead that binds thebiotin-labeled proteins and the biotin-labeled protein-SOMAmer®complexes. The free SOMAmer® reagents were then removed duringsubsequent washing steps. In the final elution step, protein-boundSOMAmer® reagents were released from their cognate proteins usingdenaturing conditions. These SOMAmer® reagents were then quantified byhybridization to custom DNA microarrays. The Cyanine-3 signal from theSOMAmer® reagent was detected on microarrays.

Clinical definition: Patients with a normal pregnancy were those whodelivered healthy term (≥37 weeks) infants whose birthweight wasappropriate for the gestational age (AGA) (10th-90th percentile). Beforethe current disclosure, spontaneous labor at term was diagnosed in thepresence of spontaneous regular uterine contractions occurring at aminimum frequency of two every 10 minutes with cervical changes thatrequired hospital admission.

Statistical analysis: Demographics data analysis: The Kolmogorov-Smirnovtest was used to assess whether the observations met a normaldistribution. Comparisons of continuous data were analyzed using theKruskal-Wallis and Mann-Whitney U tests. Statistical analysis wasperformed with SPSS software (version 19; IBM Corporation, Armonk, N.Y.)and the R statistical software. A p value of <0.05 was consideredstatistically significant.

Development of proteomics based classifiers: Since changes in proteinabundance indicative of the onset of labor were expected to be foundclose to the time of labor, the analysis of the longitudinal dataset 1was focused on the last two measurement points during pregnancy.Therefore, protein profiles at the time of labor (last sample) and themost recent sample before the onset of labor (second to last sample)were included in the analysis. The rate of the change (slope) in proteinabundance between the last two samples of each patient was calculated.LDA prediction models were built based either on the protein abundanceat the last time point or the rate of change across the last two timepoints. To allow both prediction model development and estimation ofprediction performance on dataset 1 (TIL n=20; TNL n=20), a repeated(100 iterations) hold-out cross-validation procedure was used. At eachiteration data from 75% of the subjects selected randomly was used todevelop a prediction model which was then tested on the remaining 25% ofthe subjects to compute sensitivity, specificity and area under receiveroperator characteristics (ROC) curves (AUC). These performance metricswere then averaged over the 100 iterations and reported.

Development of the LDA prediction models on the training data at eachiteration was based on a procedure similar to that described in Tarca etal., Systems Biomedicine 2013; 1(4) that has shown to perform well inmultiple international systems biology competitions in whichhigh-dimensional data was used to develop prediction models (Tarca etal., Bioinformatics 2013; 29(22):2892-9; Dayarian et al., Bioinformatics2015; 31(4):462-70). Briefly, given a training dataset (1,125 proteinsfor 15 TIL and 15 TNL patients) the proteins were ranked based onp-values from a t-test comparing the data (protein measurement at lastpoint or slope over last two time points) between groups. The top fourproteins with p<0.05 and (eventually) difference in means greater than aminimum cut-off were retained. The cut-off used amounts to 1.5 fold inaverage protein abundance between groups for the last point analysis andits equivalent for the slope based analysis. Increasing combinations ofup to four proteins were used as predictors in an LDA model, and theoptimal combination size was determined by maximizing the AUC statisticof the model evaluated by leave-one-out cross-validation on the trainingdata.

The final models (one based on slopes and one on the abundance at lastpoint) were trained with the same methodology on 100% of the dataset 1and then applied to the dataset 2 (women without spontaneous labor) toobtain a second independent estimate of specificity of these models.

Results. Clinical characteristics. The demographic and clinicalcharacteristics of the study population are shown in FIG. 8. There wereno significant differences in maternal age, pre-pregnancy body massindex (BMI), race and percentage of nulliparity between patients withand without labor in Dataset 1. Patients with term delivery andspontaneous labor had significantly higher gestational age at delivery,birthweight and cesarean delivery rate than those without spontaneouslabor (p<0.05 for all) (FIG. 8). A total of 50 patients with normalpregnancy and no spontaneous labor were included in dataset 2. Themedian (interquartile range) gestational age at delivery and birthweightwere 40.0 (39.0-40.8) weeks of gestation and 3,393 (3,101-3,620) grams,respectively. The frequency of cesarean delivery was 70% (35/50) (FIG.8).

Protein markers for spontaneous labor at term. Prediction performance ofprotein abundance at last sample: The estimated prediction performanceof a multi-marker LDA model was 60% sensitivity at 88% specificity(AUC=0.77) based on 100 iterations hold-out procedure in which 75% ofthe patients were used for training the model and 25% were used fortesting. The final model based on proteins selected on the entiredataset 1 included TF, Ran, Phosphoglycerate mutase 1, andInosine-5′-monophosphate dehydrogenase 1 (IMDH1). The longitudinalprofiles of these four proteins demonstrating the differences betweenTIL and TNL groups at the last time point are shown in FIG. 9A and FIGS.10C-10E. Of note, the four proteins selected in the prediction model arethe only ones out of the 1,125 measured that would be deemed significantin a differential abundance analysis at false discovery rate of 5%(based on two-tailed t-tests) and at least 1.5 fold change in proteinabundance between groups (at the last sample).

The leave-one-out cross-validated performance of these four proteins hada 70% sensitivity and 100% specificity (AUC=0.95). When the predictionmodel was applied on the second dataset containing data from 50 TNLpatients and obtained in another experimental batch, the predictionmodel based on absolute protein abundance had a specificity of 78%(sensitivity could not be determined since no TIL cases were availablein dataset 2).

Prediction performance of protein abundance rate of change withgestation: When the rate of change in protein abundance between last andsecond to last sample was used for analysis, the estimated predictionperformance of a multi-marker LDA model was 60% sensitivity at 94%specificity (AUC=0.85) based on 100 iterations hold-out procedure. Thefinal model based on proteins selected on the entire dataset 1 includedB7-H2, SORC2, TF, and C1-Esterase Inhibitor. The longitudinal profilesof these proteins demonstrating different rates of change from second tolast to the last sample between TIL and TNL patients are shown in FIGS.9A and 9B and FIGS. 10A and 10B. Of note, the top three of the fourproteins selected in the prediction model (B7-H2, SORC2, and TF) wouldbe deemed significant in a differential abundance analysis at falsediscovery rate of 5% (based on two-tailed t-tests) and an effect sizeequivalent to at least 1.5 fold change at the last point and sameaverage abundance at the second to last point.

The leave-one-out cross-validated performance of these four proteins had75% sensitivity and 100% specificity (AUC=0.94). When the predictionmodel was applied on the second dataset containing data from 50 TNLpatients and obtained in another experimental batch, the predictionmodel based on rate of change in protein abundance had a specificity of90% (sensitivity could not be determined since no TIL cases wereavailable in dataset 2). Of note, the largest contribution to theprediction performance of the set of four proteins is attributed toB7-H2 (FIG. 11). The calculation and distributions of the slopes forB7-H2 in the TNL and TIL patients are shown in FIG. 12.

Using two cut-offs on the estimated probability of each patient to havespontaneous labor (P_(labor)) as provided by the LDA slope model, threeprediction zones were identified: Zone A) P_(labor)<0.04, 35% (14/40) ofpatients; 93% (13/14) of these did not have spontaneous labor; Zone B)0.04≤P_(labor)<0.47, 25% (10/40) of patients; and Zone C)P_(labor)≥0.47, 40% (16/40) of patients; all of them (16/16) hadspontaneous labor (FIG. 13).

Discussion Principal findings of the Example: 1) The cross-validatedperformance of a classifier using the rate of change in proteinabundance of up to four proteins (out of 1,125 proteins available in theSomaLogic platform), had a sensitivity of 60% and a specificity of 94%(AUC=0.85) for diagnosis of term labor; 2) the performance of the finalmodel had a sensitivity of 75% and specificity of 100% (AUC=0.94) asassessed by leave-one-out cross-validation based on a fixed set of fourproteins that included B7-H2, SORC2, TF, and C1-Esterase Inhibitor; 3)B7-H2 contributed most to the predictive power of the rate of change inprotein abundance between the last two measurement points, as it had anAUC=0.88 alone; and 4) when applied to a second dataset obtained in adifferent experimental batch, the model based on the slopes of fourproteins confirmed its high specificity (90%), unlike the model builtusing the protein abundance at the last time point only (specificity of78%).

The finding of this longitudinal Example that four proteins in maternalblood (determined at 4 weeks before labor and at the time of labor) canpredict true labor with a high specificity is novel. These four proteinshave biological plausibility associated with the process of parturition.Interestingly, the change in protein abundance of B7-H2 contributed themost for the prediction of labor. This protein participates in theregulation of T-effector-cell function, Ig isotype class switching andcytokine production (IFN-γ, IL-4 and IL-10). Its effect is mainly in thedecidua and not in the maternal circulation suggestive of activation ofthe inflammatory processes leading to the onset of parturition. Changesin inflammatory system were reported in all components of the commonpathway of parturition. Indeed, labor is characterized by inflammatoryprocesses in the cervix, myometrium choriodecidua and amniotic fluid.

An additional protein that changed substantially prior to term birth istissue factor, the natural activator of the coagulation cascade. Changesin the maternal coagulation system have been reported in term andpreterm labor (e.g. Keren-Politansky et al., Thrombosis research 2014;133(4):585-9). The activation and the changes in the coagulation cascadeduring parturition results from two elements, the first is the impact ofinflammation that activates the coagulation cascade and the second isdecidual activation that releases tissue factor. The third protein thatis changing is the Sortilin-related VPS10 domain containing receptor 2(SorCS2). This protein is one a family member of vascular proteinsorting 10 and is involved in protein transcript, signal transduction,neurogenesis, and functional maintenance of the nervous system. Thefunction of SorCS2 remains to be elucidated. The last protein, plasmaprotease C1 inhibitor, is the inhibitor of the classical activation ofthe complement system. Its decrease prior to labor may enable theactivation of the complement as part of the inflammatory responseassociated with parturition. Collectively, this set of proteins thatchange in abundance with the onset of labor represent major biologicalpathways that participate in the process of human parturition.

The high specificity of the four proteins identified from the analysisherein can be used to decrease the index of suspicion for true labor.This has important clinical implications since in the presence of anegative result, patients are unlikely to deliver. Therefore, hospitaladmission and interventions such as the administration of analgesia anduterotonic agents will not be necessary. Also, compelling evidence showsthat the false diagnosis of true labor increases the rate of inductionand augmentation of labor, amniotomy, operative delivery as well ascesarean delivery.

Of the four proteins selected as predictive of spontaneous labor basedon their abundance at last sample, TF and PGAM1 exhibited consistentlower levels in the labor group than in controls only at last sample,but not earlier (FIG. 9A and FIG. 10D). In contrast, Ran and IMDH1appear to have higher levels even earlier in pregnancy (FIGS. 100 and10E), yet the within group variability in the data is high. The use ofwithin patient rate of change in protein abundance over the last twotime points (slope) enabled the discovery VPS10 domain-containingreceptor SorCS2 (SORC2) whose absolute levels do not distinguish betweengroups at any time point yet a sharp decrease in its abundance isspecific to patients in labor. Moreover, the slope based analysisreduces the patient to patient variability and increase discriminationfor proteins such as B7-H2, TF, and C1-Esterase Inhibitor which exhibitdifferences in mean abundance at the last sample.

This variability is contributed by unknown patient characteristics thataffect the absolute level of these proteins, as well by slightdifferences in gestational age at last sample. The reduction in thisunwanted variability is possible as each patient becomes its own controland is akin to the use of a fetal growth chart as opposed to a fetalsize chart for detection of growth restriction.

This Example demonstrates that the measurement of proteins in maternalblood identifies patients experiencing true labor. In particularembodiments, these protein can be used in combination with clinicalsigns and symptoms, along with other labor indicators, such as cervicallength to diagnose a patient as experiencing true or false labor. Thestrengths of this Example include longitudinal collection of samples,the use of novel proteomics technique which can measure more thanone-thousand proteins simultaneously, and the robust statistical modeldevelopment and evaluation strategy that relies on cross-validation.

As will be understood by one of ordinary skill in the art, eachembodiment disclosed herein can comprise, consist essentially of, orconsist of its particular stated element, step, ingredient or component.Thus, the terms “include” or “including” should be interpreted torecite: “comprise, consist of, or consist essentially of.” As usedherein, the transition term “comprise” or “comprises” means includes,but is not limited to, and allows for the inclusion of unspecifiedelements, steps, ingredients, or components, even in major amounts. Thetransitional phrase “consisting of” excludes any element, step,ingredient or component not specified. The transition phrase “consistingessentially of” limits the scope of the embodiment to the specifiedelements, steps, ingredients or components and to those that do notmaterially affect the embodiment. As used herein, a material effectwould cause a statistically significant reduction in the specificity ofthe assessment of the presence of true labor.

Unless otherwise indicated, all numbers used in the specification andclaims are to be understood as being modified in all instances by theterm “about.” Accordingly, unless indicated to the contrary, thenumerical parameters set forth in the specification and attached claimsare approximations that may vary depending upon the desired propertiessought to be obtained by the present invention. At the very least, andnot as an attempt to limit the application of the doctrine ofequivalents to the scope of the claims, each numerical parameter shouldat least be construed in light of the number of reported significantdigits and by applying ordinary rounding techniques. When furtherclarity is required, the term “about” has the meaning reasonablyascribed to it by a person skilled in the art when used in conjunctionwith a stated numerical value or range, i.e. denoting somewhat more orsomewhat less than the stated value or range, to within a range of ±20%of the stated value; ±19% of the stated value; ±18% of the stated value;±17% of the stated value; ±16% of the stated value; ±15% of the statedvalue; ±14% of the stated value; ±13% of the stated value; ±12% of thestated value; ±11% of the stated value; ±10% of the stated value; ±9% ofthe stated value; ±8% of the stated value; ±7% of the stated value; ±6%of the stated value; ±5% of the stated value; ±4% of the stated value;±3% of the stated value; ±2% of the stated value; or ±1% of the statedvalue.

Notwithstanding that the numerical ranges and parameters setting forththe broad scope of the invention are approximations, the numericalvalues set forth in the specific examples are reported as precisely aspossible. Any numerical value, however, inherently contains certainerrors necessarily resulting from the standard deviation found in theirrespective testing measurements.

The terms “a,” “an,” “the” and similar referents used in the context ofdescribing the invention (especially in the context of the followingclaims) are to be construed to cover both the singular and the plural,unless otherwise indicated herein or clearly contradicted by context.Recitation of ranges of values herein is merely intended to serve as ashorthand method of referring individually to each separate valuefalling within the range. Unless otherwise indicated herein, eachindividual value is incorporated into the specification as if it wereindividually recited herein. All methods described herein can beperformed in any suitable order unless otherwise indicated herein orotherwise clearly contradicted by context. The use of any and allexamples, or exemplary language (e.g., “such as”) provided herein isintended merely to better illuminate the invention and does not pose alimitation on the scope of the invention otherwise claimed. No languagein the specification should be construed as indicating any non-claimedelement essential to the practice of the invention.

Groupings of alternative elements or embodiments of the inventiondisclosed herein are not to be construed as limitations. Each groupmember may be referred to and claimed individually or in any combinationwith other members of the group or other elements found herein. It isanticipated that one or more members of a group may be included in, ordeleted from, a group for reasons of convenience and/or patentability.When any such inclusion or deletion occurs, the specification is deemedto contain the group as modified thus fulfilling the written descriptionof all Markush groups used in the appended claims.

Certain embodiments of this invention are described herein, includingthe best mode known to the inventors for carrying out the invention. Ofcourse, variations on these described embodiments will become apparentto those of ordinary skill in the art upon reading the foregoingdescription. The inventor expects skilled artisans to employ suchvariations as appropriate, and the inventors intend for the invention tobe practiced otherwise than specifically described herein. Accordingly,this invention includes all modifications and equivalents of the subjectmatter recited in the claims appended hereto as permitted by applicablelaw. Moreover, any combination of the above-described elements in allpossible variations thereof is encompassed by the invention unlessotherwise indicated herein or otherwise clearly contradicted by context.

Furthermore, numerous references have been made to publications, patentsand/or patent applications (collectively “references”) throughout thisspecification. Each of the cited references is individually incorporatedherein by reference for their particular cited teachings.

The particulars shown herein are by way of example and for purposes ofillustrative discussion of the preferred embodiments of the presentinvention only and are presented in the cause of providing what isbelieved to be the most useful and readily understood description of theprinciples and conceptual aspects of various embodiments of theinvention. In this regard, no attempt is made to show structural detailsof the invention in more detail than is necessary for the fundamentalunderstanding of the invention, the description taken with the drawingsand/or examples making apparent to those skilled in the art how theseveral forms of the invention may be embodied in practice.

Definitions and explanations used in the present disclosure are meantand intended to be controlling in any future construction unless clearlyand unambiguously modified in the examples or when application of themeaning renders any construction meaningless or essentially meaningless.In cases where the construction of the term would render it meaninglessor essentially meaningless, the definition should be taken fromWebster's Dictionary, 3rd Edition or a dictionary known to those ofordinary skill in the art, such as the Oxford Dictionary of Biochemistryand Molecular Biology (Ed. Anthony Smith, Oxford University Press,Oxford, 2004).

In closing, it is to be understood that the embodiments of the inventiondisclosed herein are illustrative of the principles of the presentinvention. Other modifications that may be employed are within the scopeof the invention. Thus, by way of example, but not of limitation,alternative configurations of the present invention may be utilized inaccordance with the teachings herein. Accordingly, the present inventionis not limited to that precisely as shown and described.

What is claimed is:
 1. A method, comprising: obtaining a sample from apregnant female experiencing symptoms associated with false labor andtrue labor; and assessing the sample for abundance of tissue factor (TF)in combination with (i) ICOS Ligand (B7-H2), VPS10 Domain ContainingReceptor SorCS2 (SORC2) and/or Plasma Protease C1 Inhibitor (C1-EsteraseInhibitor); or (ii) Ras-related Nuclear protein (Ran),Inosine-5′-monophosphate dehydrogenase (IMDH1), and/or Phosphoglyceratemutase 1 (PGAM1).
 2. The method of claim 1, comprising: (a) assessing TFin combination with B7-H2, SORC2, and C1-Esterase Inhibitor; or (b)assessing TF in combination with Ran, IMDH1, and PGAM1; or (c) both (a)and (b).
 3. The method of claim 2(a) or claim 2(c), further comprisingcalculating a rate of change of abundance of TF, B7-H2, SORC2, andC1-Esterase Inhibitor between two samples obtained from the samepregnant female at two different times.
 4. The method of claim 3,wherein the calculating of the rate of change of abundance of TF, B7-H2,SORC2, and C1-Esterase Inhibitor is the formula:(Abundance_(T2)−Abundance_(T1))/(GA_(T2)−GA_(T1)), wherein GA refers togestational age and can be measured in weeks.
 5. The method of claim 4,wherein: (a) GA_(T2) and GA_(T1) are between 33 and 40 weeks; and/or (b)the rate of change of abundance is used to calculate a true labor score(1), according to the formula:true labor score(1)=4.4*S _((B7-H2))−5.9*S _((Sorc2))−0.5*S_((TF))−0.03*S _((C1-Esterase Inhibitor))+1.1,ortrue labor score(1)=4.422*S _((B7-H2))−5.981*S _((Sorc2))−0.538*S_((TF))−0.033*S _((C1-Esterase Inhibitor))+1.134, wherein S refers tothe rate of change of abundance.
 6. The method of claim 2, wherein: thesample obtained from a pregnant female is collected between 33 and 40weeks of the female's pregnancy; and/or the sample is obtained when thepregnant female is experiencing symptoms associated with false labor andtrue labor; and/or the symptoms are contractions; and/or the samples areplasma or serum samples.
 7. The method of claim 2(b) or 2(c), furthercomprising calculating changes in abundance (CA) of TF, Ran, IMDH1, andPGAM1 as compared to a value derived from a reference population thathas not experienced true labor.
 8. The method of claim 7, comprisinginstructions that direct: calculation of true labor score(2), whereinCA_(TF), CA_(Ran), CA_(IMDH1), and CA_(PGAM1) are used in a LinearDiscriminant Analysis Model; and/or analysis of true labor score(2);wherein the true labor score(2) comprises two threshold values, X and Y,wherein a true labor score(2)≤X denotes that the pregnant female is infalse labor, and a true labor score(2)>Y denotes that the pregnantfemale is in true labor.